Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach

• URL: http://arxiv.org/abs/2102.04170v1
• Date: Mon, 8 Feb 2021 12:53:32 GMT
• Title: Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach
• Authors: Jiawei Shao, Yuyi Mao, and Jun Zhang
• Abstract summary: A low-end edge device transmits the extracted feature vector of a local data sample to a powerful edge server for processing. It is critical to encode the data into an informative and compact representation for low-latency inference given the limited bandwidth. We propose a learning-based communication scheme that jointly optimize feature extraction, source coding, and channel coding.
• Score: 3.983055670167878
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper investigates task-oriented communication for edge inference, where a low-end edge device transmits the extracted feature vector of a local data sample to a powerful edge server for processing. It is critical to encode the data into an informative and compact representation for low-latency inference given the limited bandwidth. We propose a learning-based communication scheme that jointly optimizes feature extraction, source coding, and channel coding in a task-oriented manner, i.e., targeting the downstream inference task rather than data reconstruction. Specifically, we leverage an information bottleneck (IB) framework to formalize a rate-distortion tradeoff between the informativeness of the encoded feature and the inference performance. As the IB optimization is computationally prohibitive for the high-dimensional data, we adopt a variational approximation, namely the variational information bottleneck (VIB), to build a tractable upper bound. To reduce the communication overhead, we leverage a sparsity-inducing distribution as the variational prior for the VIB framework to sparsify the encoded feature vector. Furthermore, considering dynamic channel conditions in practical communication systems, we propose a variable-length feature encoding scheme based on dynamic neural networks to adaptively adjust the activated dimensions of the encoded feature to different channel conditions. Extensive experiments evidence that the proposed task-oriented communication system achieves a better rate-distortion tradeoff than baseline methods and significantly reduces the feature transmission latency in dynamic channel conditions.

Related papers

• Tackling Distribution Shifts in Task-Oriented Communication with Information Bottleneck [28.661084093544684]
  We propose a novel approach based on the information bottleneck (IB) principle and invariant risk minimization (IRM) framework. The proposed method aims to extract compact and informative features that possess high capability for effective domain-shift generalization. We show that the proposed scheme outperforms state-of-the-art approaches and achieves a better rate-distortion tradeoff.
  arXiv  Detail & Related papers  (2024-05-15T17:07:55Z)
• Communication-Efficient Framework for Distributed Image Semantic Wireless Transmission [68.69108124451263]
  Federated learning-based semantic communication (FLSC) framework for multi-task distributed image transmission with IoT devices. Each link is composed of a hierarchical vision transformer (HVT)-based extractor and a task-adaptive translator. Channel state information-based multiple-input multiple-output transmission module designed to combat channel fading and noise.
  arXiv  Detail & Related papers  (2023-08-07T16:32:14Z)
• Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity [72.85248553787538]
  This paper focuses on performance analysis and optimization for wireless FL, considering data heterogeneity, combined with wireless resource allocation. We formulate the loss function minimization problem, under constraints on long-term energy consumption and latency, and jointly optimize client scheduling, resource allocation, and the number of local training epochs (CRE) Experiments on real-world datasets demonstrate that the proposed algorithm outperforms other benchmarks in terms of the learning accuracy and energy consumption.
  arXiv  Detail & Related papers  (2023-08-04T04:18:01Z)
• Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme [17.4244108919728]
  To serve both the data transmission and semantic tasks, joint data compression and semantic analysis has become pivotal issue in semantic communications. This paper proposes a deep separate source-channel coding framework for the joint task and data oriented semantic communications. An iterative training algorithm is proposed to tackle the overfitting issue of deep learning models.
  arXiv  Detail & Related papers  (2023-02-27T08:34:37Z)
• Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications [87.05763097471487]
  This paper aims to design robust Edge Intelligence using semantic communication for time-critical IoT applications. We analyze the effect of image DCT coefficients on inference accuracy and propose the channel-agnostic effectiveness encoding for offloading.
  arXiv  Detail & Related papers  (2022-11-24T20:13:17Z)
• Toward Adaptive Semantic Communications: Efficient Data Transmission via Online Learned Nonlinear Transform Source-Channel Coding [11.101344530143303]
  We propose an online learned joint source and channel coding approach that leverages the deep learning model's overfitting property. Specifically, we update the off-the-shelf pre-trained models after deployment in a lightweight online fashion to adapt to the distribution shifts in source data and environment domain. We take the overfitting concept to the extreme, proposing a series of implementation-friendly methods to adapt the model or representations to an individual data or channel state instance.
  arXiv  Detail & Related papers  (2022-11-08T16:00:27Z)
• Robust Information Bottleneck for Task-Oriented Communication with Digital Modulation [31.39386509261528]
  Task-oriented communications, mostly using learning-based joint source-channel coding (JSCC), aim to design a communication-efficient edge inference system. We develop a robust encoding framework, named robust information bottleneck (RIB), to improve the communication robustness to the channel variations. The proposed DT-JSCC achieves better inference performance than the baseline methods with low communication latency.
  arXiv  Detail & Related papers  (2022-09-21T14:21:14Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• Adaptive Information Bottleneck Guided Joint Source and Channel Coding for Image Transmission [132.72277692192878]
  An adaptive information bottleneck (IB) guided joint source and channel coding (AIB-JSCC) is proposed for image transmission. The goal of AIB-JSCC is to reduce the transmission rate while improving the image reconstruction quality. Experimental results show that AIB-JSCC can significantly reduce the required amount of transmitted data and improve the reconstruction quality.
  arXiv  Detail & Related papers  (2022-03-12T17:44:02Z)
• Context-Preserving Instance-Level Augmentation and Deformable Convolution Networks for SAR Ship Detection [50.53262868498824]
  Shape deformation of targets in SAR image due to random orientation and partial information loss is an essential challenge in SAR ship detection. We propose a data augmentation method to train a deep network that is robust to partial information loss within the targets.
  arXiv  Detail & Related papers  (2022-02-14T07:01:01Z)
• Task-Oriented Communication for Multi-Device Cooperative Edge Inference [14.249444124834719]
  cooperative edge inference can overcome the limited sensing capability of a single device, but it substantially increases the communication overhead and may incur excessive latency. We propose a learning-based communication scheme that optimize local feature extraction and distributed feature encoding in a task-oriented manner.
  arXiv  Detail & Related papers  (2021-09-01T03:56:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.